Integrated quantitative fractal polarimetric analysis of monolayer lung cancer cells

Shrestha, Suman; Zhang, Lin; Quang, Timothy; Farrahi, Tannaz; Narayan, Chaya; Deshpande, Aditi; Ying, Nie; Blinzler, A.; Ma, Jun‐Yu; Liu, Bo; Giakos, G.C.

doi:10.1117/12.2053596

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…As a result, under backscattered geometry, multiple kinds of early-stage malignancies (cancer) could be discriminated, considering and quantifying their unique diffuse reflectance polarimetric signatures. Another related method uses polarimetric discrimination of the wavelet-fractal domain for histological analysis of monolayer lung cancer cells [31,32]. Wavelet polarimetric evaluation of healthy, squamous carcinoma, and adenocarcinoma lung tissue cell lines proves quite promising and reliable in accurately and robustly classifying cells as healthy or cancerous ones, in conjunction with proper discrimination between malignant cells originating from different types of lung cancer [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Deshpande

Cambria

Barnes

et al. 2021

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This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation.

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Section: Literature Reviewmentioning

confidence: 99%

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Deshpande

Cambria

Barnes

et al. 2021

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“…Since 40 years of its applications, it has become an important basis for the analysis and diagnosis of clinical pathology and widely used in markers of angiogenesis [6], the human protein atlas [26], atherosclerosis progression [20], the dynamics of skin inflammation [23], pulmonary nodules [33], and COPD [35], particularly plays an important role in tumor/cancer qualitative, quantitative, classification, prognosis and diagnosis [1,4,5,8,11,12,18,32,43,44]. Scholars such as Irshad et al [13,14] took immunohistochemistry as key and gave the development direction in digital processing.…”

Section: Introductionmentioning

confidence: 99%

Extraction of target region in lung immunohistochemical image based on artificial neural network

Fan

Cao

2016

Multimed Tools Appl

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Immunohistochemistry is widely used in clinical pathology analysis and diagnosis, the target regions segmentation is the key procedure and always provides support for many qualitative and quantitative analyses on digitized immunohistochemical image. In lung tissue immunohistochemistry applications, the target region needs to be extracted out of the whole image firstly. Most existing methods based on color cannot fulfill the extraction of antibody region. Thus, there is a need of effective extraction method. Methods: According to the features of target region in images to be processed, this paper presents a solution framework based on artificial neural network (ANN). Results: Six effective features of the candidate regions are analyzed and extracted as the inputs of the ANN; three-layers back propagation neural network with six inputs and one output is constructed, and ANN's parameters are trained by the learning image set. By the trained ANN, target region core are obtained and then expanded to the whole target region through conditional expansion. Conclusion: Through testing the framework by testing image set and comparing with the main existing methods, it can be concluded that the proposed framework can remove non-target regions and extract the target regions well, while the contrast methods cannot remove all the non-target regions. Significance: The method presented in this paper has practical and potential significance to realize automated and quantitative tissue immunohistochemical image analysis.

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Design, Calibration, and Testing of an Automated Near-Infrared Liquid-Crystal Polarimetric Imaging System for Discrimination of Lung Cancer Cells

Shrestha

Petermann

Farrahi

et al. 2015

IEEE Trans. Instrum. Meas.

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Integrated quantitative fractal polarimetric analysis of monolayer lung cancer cells

Cited by 3 publications

References 19 publications

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Extraction of target region in lung immunohistochemical image based on artificial neural network

Design, Calibration, and Testing of an Automated Near-Infrared Liquid-Crystal Polarimetric Imaging System for Discrimination of Lung Cancer Cells

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